Adversarial Attacks on Automatic Speech Recognition (ASR): A Survey

Abstract

Automatic Speech Recognition (ASR) systems have improved and eased how humans interact with devices. ASR system converts an acoustic waveform into the relevant text form. Modern ASR inculcates deep neural networks (DNNs) to provide faster and better results. As the use of DNN continues to expand, there is a need for examination against various adversarial attacks. Adversarial attacks are synthetic samples crafted carefully by adding particular noise to legitimate examples. They are imperceptible, yet they prove catastrophic to DNNs. Recently, adversarial attacks on ASRs have increased but previous surveys lack generalization of the different methods used for attacking ASR, and the scope of the study is narrowed to a particular application, making it difficult to determine the relationships and trade-offs between the attack techniques. Therefore, this survey provides a taxonomy illustrating the classification of the adversarial attacks on ASR based on their characteristics and behavior. Additionally, we have analyzed the existing methods for generating adversarial attacks and presented their comparative analysis. We have clearly drawn the outline to indicate the efficiency of the adversarial techniques, and based on the lacunae found in the existing studies, we have stated the future scope.

Keywords

• Adversarial attacks
• adversarial samples
• automatic speech recognition (ASR)
• deep neural network (DNN)